AI & ML Site Reliability Engineer - **Must be in Ireland** (open to Remote or Hybrid)
We are seeking an experienced AI & ML Site Reliability Engineer to design, build, and maintain infrastructure supporting AI, machine learning, and data-driven solutions. This role involves architecting scalable systems, managing AI deployments, and optimizing data platforms to enable impactful business outcomes. You?ll work closely with data scientists, engineers, and cross-functional teams to ensure reliability, scalability, and efficiency throughout the AI lifecycle.
Key Responsibilities:
- AI/ML Infrastructure Design:Build scalable, secure, and efficient AI systems for training and deploying ML models.
- Data Platform Development:Develop and maintain a centralized big data platform to support analytics and AI solutions.
- Vector Databases & Knowledge Graphs:Implement and manage databases for high-dimensional data and structured/unstructured data integration.
- Model Deployment & Optimization:Deploy, monitor, and optimize ML models for performance and scalability.
- CI/CD Pipelines:Design and implement CI/CD processes tailored for ML workflows, ensuring reproducibility and automation.
- RAG & Advanced Techniques:Develop retrieval-augmented generation (RAG) systems, including advanced reasoning systems like GraphRAG.
- Monitoring & Governance:Establish protocols for model performance monitoring, retraining, and ethical AI governance.
Qualifications:
- Education:Bachelor?s degree in Computer Science, Data Science, or related field.
- Experience:
- 5+ years in site reliability, DevOps, or ML Ops roles.
- Experience with cloud platforms (AWS, GCP, Azure) and AI/ML tools (e.g., TensorFlow, PyTorch).
- Proven track record deploying ML models (regressions, neural networks, etc.) in production environments.
- Technical Expertise:
- Proficiency in Python, Bash, and related ML libraries.
- Experience with data tools like Apache Spark, Hadoop, or Airflow.
- Knowledge of vector databases (e.g., Pinecone, Milvus) and knowledge graph tools (e.g., Neo4j).
- Familiarity with containerization (Docker, Kubernetes) and CI/CD for ML pipelines.
- Behavioral Skills:
- Problem-solving mindset and intellectual curiosity.
- Strong collaboration and communication abilities.
- Entrepreneurial spirit and adaptability.
Preferred Qualifications:
- Master?s degree in a relevant field.
- Experience in private LLM fine-tuning, edge AI deployments, or advanced data infrastructure.
- Knowledge of AI ethics and risk management.
